Conventional fallen and falling detection systems come in three categories, namely stationary, portable, and mixed. A conventional stationary fallen and falling detection system comprises a sensing device disposed, in a fixed manner or in an embedded manner, in an environment where detection is to take place. The conventional stationary fallen and falling detection system has the following advantages: it has a simple structure, and it does not require a user to wear a sensing device on the user's body. The conventional stationary fallen and falling detection system has the following disadvantages: it infringes upon individuals' privacy, its detection fails to cover dead spaces, and it incurs high infrastructure costs in effectuating installations at multiple locations, because users are never stationary. The conventional portable fallen and falling detection system has to be worn by a user or fastened to an object and thus its range of detection is restricted to the user. Hence, the conventional portable fallen and falling detection system is advantageously characterized by its being compact, portable, and cheap, but is disadvantageously characterized in that the user may forget to wear it or is unable to wear it at a specific place, such as a toilet or a bathroom. The conventional mixed fallen and falling detection system, which is basically a combination of the stationary and portable fallen and falling detection systems, is effective in detecting a fall but shares all the aforesaid disadvantages of the conventional stationary fallen and falling detection system and the conventional portable fallen and falling detection system.
The conventional portable fallen and falling detection system comprises an accelerometer, a gyroscope, and a horizon sensor for use in dynamic sensing. It is most notably effective in calculating a signal vector magnitude (SVM) in a way proposed by Mathie, with the equation
            S      ⁢                          ⁢      V      ⁢                          ⁢      M        =                            a          ⁢                                          ⁢                      x            2                          +                  a          ⁢                                          ⁢                      y            2                          +                  a          ⁢                                          ⁢                      z            2                                ,where ax, ay, az denotes the acceleration along the x-axis, y-axis, and z-axis, respectively. As predicated by the equation, the chance that a fall has happened is high whenever SVM>2.8 g. The Mathie estimation is simple, accurate, and insusceptible to direction-specific errors. However, the Mathie estimation is not effective in discerning taking a seat quickly, assuming a lying posture quickly, running, and leaping.
As regards detection by gyroscope, Nyan Tay puts forth a two-axis gyroscope approach which involves affixing gyroscopes to the chest, the front of the abdomen, and the right forearm, respectively, so as to detect falling backward, falling sideward, and general daily motions, such as standing up, walking, and bending forward to pick up an object, lying down, and a sit-up. In doing so, Nyan Tay identifies the angular velocity thresholds of the chest, the front of the abdomen, and the right forearm, with a sensitivity of 100% and a specificity of 84%. Furthermore, the sensors used in the Nyan Tay technique predict a fall 200 milliseconds before the falls happens. In 2008, Nyan Tay assessed the consistency in angular velocity between the trunk and the thigh with a view to determining whether a fall had occurred; in doing so, Nyan Tay not only discovered that both the sensitivity and specificity are 100% but also reduced the time taken to predict a fall to 700 milliseconds.
Although research is conducted on conventional accelerometer and gyroscope detection systems fully and fruitfully, it has hitherto failed to address those falls which are difficult to predict accurately, including taking a seat quickly, assuming a lying posture quickly, and falling into a coma at a seat.
The surge of smartphone and dynamic wearable sensors began in 2005. Tong Zhang puts forth connecting a cell phone to the Interne, using grouping algorithm in two stages. The first stage involves using 1-Class SVM (Support Vector Machine), and the second stage involves using KFD technique (Kernel Fisher Discriminant) and K-NN technique (Nearest Neighbor) in detecting a fall, resulting in a sensitivity of 93.3%. Afterward, Dai Jiangpeng proposes the equation
                          A        T                    =                                                                  A              ⁢                                                          ⁢              x                                            2                +                                                        A              ⁢                                                          ⁢              y                                            2                +                                                        A              ⁢                                                          ⁢              z                                            2                      ,where Ax, Ay, Az denote the acceleration along the x-axis, y-axis, and z-axis, respectively, and proposes |AV|=|Ax sin θz+Ay sin θy−Ay cos θy cos θz|, where θx, θy, θz denote the angle of rotation about x-axis, y-axis, and z-axis, respectively, so as to determine their thresholds, respectively, but such an approach is inapplicable to the elderly who seldom carry a cell phone, not to mention that the elderly are likely to fall while using a cell phone or lose their grip on a cell phone while falling.
A new feasible trend lies in the use of at least three sensors. In Taiwan, Wang Zhizhong from the National Chiao Tung University uses an optical motion image capturing system in measuring inertial acceleration, wherein six cameras are disposed in a 2×3 rectangular array, a plurality of reflecting labels is adhered to different points along the cervical vertebrae to therefore acquire the total acceleration of 0.85 g for use a fall threshold, and an electromyography (EMG) system operates in a manner that electromyography patches are affixed to eight points of the human body, namely deltoid muscle of the upper limbs, trapezius muscle of the upper limbs, tibialis anterior muscle of the lower limbs, and gastrocnemius muscle of the lower limbs, for measuring the average maximum peak values and standard deviation of the muscular strength of the human body in daily life, wherein the fall threshold is set to the sum of the average peak value and a twofold deviation. If three of the aforesaid muscles reach the aforesaid threshold in 200 milliseconds, a fall will be predicted at a sensitivity of 95.92% and a specificity of 95.42%. A research team at the National Cheng Kung University in Taiwan integrates a three-axis accelerometer and a three-axis gyroscope to retrieve information pertaining to six axes, affixes sensors to the waist and the two knees of a subject, wherein the origin is set to a point of the waist on which a light beam is projected, so as to measure an angle θ and a distance d of a leg projection point and a waist projection point relative to the origin, and thus identify a motion path (Dwf, Awf), wherein Dwf denotes the distance between the leg projection point and the waist projection point, and Awf denotes the angle of the waist projection point relative to the leg projection point, so as to take the samples of data pertaining to the motion path within three seconds. Then, the sampled data are processed with a subtractive clustering method of a neural algorithm, wherein the upper and lower limit multiples of the potential values are set to 0.5˜0.15, so as to determine whether the same motion and equilibrium related information persists, using acceleration SVM and acceleration variation rate
            A      V        =                                  ⅆ                      (                                                                                A                    x                    2                                                        (                    t                    )                                                  +                                                      A                    y                    2                                                        (                    t                    )                                                  +                                                      A                    z                    2                                                        (                    t                    )                                                                        )                                    ⅆ          t                            ,wherein, if Av is overly large and the subject fails to keep the balance of his or her body, it can be determined that the subject is going to fall, at a sensitivity of 97%. The benefits of multiple dynamic sensors include: accurate prediction of a fall, acquisition of plenty information pertaining to posture and motion, and detection of the other abnormal behavior. However, the use of an increasing number of sensors is accompanied by an increase in costs and an increase in the inconvenience brought to users who wear the sensors.
In view of this, sensor category and the location to wear the sensors are of vital importance, as these are two factors in the required data to be detected, the way of conducting an analysis, the performance, and the result. Users always favor sensors which can be worn on their bodies conveniently and comfortably, and Users always want to wear as few sensors as possible. Manufacturers are interested in cutting their manufacturing costs by reducing the categories and quantity of sensors.